Overview

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Dataset statistics

Number of variables19
Number of observations550
Missing cells573
Missing cells (%)5.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory298.5 KiB
Average record size in memory555.7 B

Variable types

Text1
DateTime3
Categorical4
Numeric11

Dataset

DescriptionVIDA 008 - Vaccine-related anthropometrics
CreatorRP2 Clinical Data Harmonization Project
URLHEAT Research Projects

Variable descriptions

study_sourceSource study identifier
CD4 cell count (cells/µL)CD4+ T lymphocyte count - immune function indicator
HIV viral load (copies/mL)HIV RNA copies per mL - treatment efficacy marker
Albumin (g/dL)Serum albumin - liver function and nutritional status
primary_datePrimary date of measurement/visit
Age (at enrolment)Patient age at study enrollment

Alerts

BMI (kg/m²) is highly overall correlated with Last weight recorded (kg) and 2 other fieldsHigh correlation
Height is highly overall correlated with Height (m) and 1 other fieldsHigh correlation
Height (m) is highly overall correlated with Height and 1 other fieldsHigh correlation
Last height recorded (m) is highly overall correlated with Height and 3 other fieldsHigh correlation
Last weight recorded (kg) is highly overall correlated with BMI (kg/m²) and 3 other fieldsHigh correlation
Patient ID is highly overall correlated with Race and 4 other fieldsHigh correlation
Race is highly overall correlated with Patient IDHigh correlation
Sex is highly overall correlated with Last height recorded (m) and 1 other fieldsHigh correlation
Weight (kg) is highly overall correlated with BMI (kg/m²) and 2 other fieldsHigh correlation
month is highly overall correlated with season and 1 other fieldsHigh correlation
original_record_index is highly overall correlated with Patient ID and 2 other fieldsHigh correlation
season is highly overall correlated with Patient ID and 2 other fieldsHigh correlation
weight is highly overall correlated with BMI (kg/m²) and 2 other fieldsHigh correlation
year is highly overall correlated with Last height recorded (m) and 4 other fieldsHigh correlation
Sex is highly imbalanced (56.9%)Imbalance
Height has 9 (1.6%) missing valuesMissing
BMI (kg/m²) has 9 (1.6%) missing valuesMissing
Height (m) has 9 (1.6%) missing valuesMissing
Last height recorded (m) has 270 (49.1%) missing valuesMissing
Last weight recorded (kg) has 265 (48.2%) missing valuesMissing
BMI (kg/m²) is highly skewed (γ1 = 23.25939817)Skewed
original_record_index is uniformly distributedUniform
anonymous_patient_id has unique valuesUnique
Patient ID has unique valuesUnique
original_record_index has unique valuesUnique

Reproduction

Analysis started2025-11-11 11:20:56.518083
Analysis finished2025-11-11 11:28:18.526333
Duration7 minutes and 22.01 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct550
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size39.7 KiB
2025-11-11T13:28:18.884404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters9350
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique550 ?
Unique (%)100.0%

Sample

1st rowHEAT_49477AEB64E0
2nd rowHEAT_31A1934F78E6
3rd rowHEAT_BAEF29F1C6B2
4th rowHEAT_2E24227DBBA0
5th rowHEAT_507153987D52
ValueCountFrequency (%)
heat_6f919c13c02e1
 
0.2%
heat_3a0a597f2ab21
 
0.2%
heat_49477aeb64e01
 
0.2%
heat_31a1934f78e61
 
0.2%
heat_baef29f1c6b21
 
0.2%
heat_2e24227dbba01
 
0.2%
heat_1e7f457a60ea1
 
0.2%
heat_86d07dc1b1561
 
0.2%
heat_cd1ef0ad4e651
 
0.2%
heat_0bc68c9b977a1
 
0.2%
Other values (540)540
98.2%
2025-11-11T13:28:19.981156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A940
 
10.1%
E922
 
9.9%
H550
 
5.9%
T550
 
5.9%
_550
 
5.9%
7442
 
4.7%
5436
 
4.7%
1432
 
4.6%
4426
 
4.6%
0425
 
4.5%
Other values (9)3677
39.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)9350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A940
 
10.1%
E922
 
9.9%
H550
 
5.9%
T550
 
5.9%
_550
 
5.9%
7442
 
4.7%
5436
 
4.7%
1432
 
4.6%
4426
 
4.6%
0425
 
4.5%
Other values (9)3677
39.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A940
 
10.1%
E922
 
9.9%
H550
 
5.9%
T550
 
5.9%
_550
 
5.9%
7442
 
4.7%
5436
 
4.7%
1432
 
4.6%
4426
 
4.6%
0425
 
4.5%
Other values (9)3677
39.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A940
 
10.1%
E922
 
9.9%
H550
 
5.9%
T550
 
5.9%
_550
 
5.9%
7442
 
4.7%
5436
 
4.7%
1432
 
4.6%
4426
 
4.6%
0425
 
4.5%
Other values (9)3677
39.3%

primary_date
Date

Primary date of measurement/visit

Distinct117
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
Minimum2020-04-22 00:00:00
Maximum2021-08-10 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T13:28:20.518878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:28:21.722278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

year
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size32.8 KiB
2020
397 
2021
153 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2200
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020397
72.2%
2021153
 
27.8%

Length

2025-11-11T13:28:22.780329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T13:28:23.288528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2020397
72.2%
2021153
 
27.8%

Most occurring characters

ValueCountFrequency (%)
21100
50.0%
0947
43.0%
1153
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21100
50.0%
0947
43.0%
1153
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21100
50.0%
0947
43.0%
1153
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21100
50.0%
0947
43.0%
1153
 
7.0%

month
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1781818
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2025-11-11T13:28:23.724742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median5
Q36
95-th percentile7
Maximum8
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.139247
Coefficient of variation (CV)0.22000908
Kurtosis0.42421336
Mean5.1781818
Median Absolute Deviation (MAD)1
Skewness-0.079123414
Sum2848
Variance1.2978838
MonotonicityNot monotonic
2025-11-11T13:28:24.064096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5217
39.5%
6134
24.4%
4105
19.1%
752
 
9.5%
319
 
3.5%
812
 
2.2%
211
 
2.0%
ValueCountFrequency (%)
211
 
2.0%
319
 
3.5%
4105
19.1%
5217
39.5%
6134
24.4%
752
 
9.5%
812
 
2.2%
ValueCountFrequency (%)
812
 
2.2%
752
 
9.5%
6134
24.4%
5217
39.5%
4105
19.1%
319
 
3.5%
211
 
2.0%

season
Categorical

High correlation 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size33.8 KiB
Autumn
341 
Winter
198 
Summer
 
11

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters3300
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAutumn
2nd rowAutumn
3rd rowAutumn
4th rowAutumn
5th rowAutumn

Common Values

ValueCountFrequency (%)
Autumn341
62.0%
Winter198
36.0%
Summer11
 
2.0%

Length

2025-11-11T13:28:24.670200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T13:28:25.229351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
autumn341
62.0%
winter198
36.0%
summer11
 
2.0%

Most occurring characters

ValueCountFrequency (%)
u693
21.0%
t539
16.3%
n539
16.3%
m363
11.0%
A341
10.3%
e209
 
6.3%
r209
 
6.3%
W198
 
6.0%
i198
 
6.0%
S11
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)3300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u693
21.0%
t539
16.3%
n539
16.3%
m363
11.0%
A341
10.3%
e209
 
6.3%
r209
 
6.3%
W198
 
6.0%
i198
 
6.0%
S11
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u693
21.0%
t539
16.3%
n539
16.3%
m363
11.0%
A341
10.3%
e209
 
6.3%
r209
 
6.3%
W198
 
6.0%
i198
 
6.0%
S11
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u693
21.0%
t539
16.3%
n539
16.3%
m363
11.0%
A341
10.3%
e209
 
6.3%
r209
 
6.3%
W198
 
6.0%
i198
 
6.0%
S11
 
0.3%

Age (at enrolment)
Real number (ℝ)

Patient age at study enrollment

Distinct536
Distinct (%)97.6%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean39.057853
Minimum20.016667
Maximum64.313889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2025-11-11T13:28:25.865945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20.016667
5-th percentile25.982222
Q131.286111
median37.813889
Q345.572222
95-th percentile56.433889
Maximum64.313889
Range44.297222
Interquartile range (IQR)14.286111

Descriptive statistics

Standard deviation9.3979918
Coefficient of variation (CV)0.24061722
Kurtosis-0.53872734
Mean39.057853
Median Absolute Deviation (MAD)7.0583333
Skewness0.4791322
Sum21442.761
Variance88.322249
MonotonicityNot monotonic
2025-11-11T13:28:26.330181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.763888892
 
0.4%
42.822222222
 
0.4%
42.313888892
 
0.4%
40.419444442
 
0.4%
36.32
 
0.4%
25.044444442
 
0.4%
31.830555562
 
0.4%
35.097222222
 
0.4%
36.905555562
 
0.4%
38.277777782
 
0.4%
Other values (526)529
96.2%
ValueCountFrequency (%)
20.016666671
0.2%
23.163888891
0.2%
23.416666671
0.2%
23.505555561
0.2%
23.6751
0.2%
23.761111111
0.2%
23.983333331
0.2%
24.041666671
0.2%
24.136111111
0.2%
24.191666671
0.2%
ValueCountFrequency (%)
64.313888891
0.2%
63.697222221
0.2%
63.41
0.2%
62.747222221
0.2%
61.713888891
0.2%
61.394444441
0.2%
61.286111111
0.2%
61.211111111
0.2%
60.6251
0.2%
60.611111111
0.2%

Sex
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size33.7 KiB
Female
453 
Male
95 
Not available
 
1

Length

Max length13
Median length6
Mean length5.6666667
Min length4

Characters and Unicode

Total characters3111
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowMale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female453
82.4%
Male95
 
17.3%
Not available1
 
0.2%
(Missing)1
 
0.2%

Length

2025-11-11T13:28:26.933507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T13:28:27.434435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female453
82.4%
male95
 
17.3%
not1
 
0.2%
available1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e1002
32.2%
a551
17.7%
l550
17.7%
F453
14.6%
m453
14.6%
M95
 
3.1%
N1
 
< 0.1%
o1
 
< 0.1%
t1
 
< 0.1%
1
 
< 0.1%
Other values (3)3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1002
32.2%
a551
17.7%
l550
17.7%
F453
14.6%
m453
14.6%
M95
 
3.1%
N1
 
< 0.1%
o1
 
< 0.1%
t1
 
< 0.1%
1
 
< 0.1%
Other values (3)3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1002
32.2%
a551
17.7%
l550
17.7%
F453
14.6%
m453
14.6%
M95
 
3.1%
N1
 
< 0.1%
o1
 
< 0.1%
t1
 
< 0.1%
1
 
< 0.1%
Other values (3)3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1002
32.2%
a551
17.7%
l550
17.7%
F453
14.6%
m453
14.6%
M95
 
3.1%
N1
 
< 0.1%
o1
 
< 0.1%
t1
 
< 0.1%
1
 
< 0.1%
Other values (3)3
 
0.1%

Race
Categorical

High correlation 

Distinct5
Distinct (%)0.9%
Missing1
Missing (%)0.2%
Memory size33.3 KiB
Black
406 
White
60 
Asian
53 
Other
 
16
Coloured
 
14

Length

Max length8
Median length5
Mean length5.0765027
Min length5

Characters and Unicode

Total characters2787
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWhite
2nd rowOther
3rd rowWhite
4th rowBlack
5th rowWhite

Common Values

ValueCountFrequency (%)
Black406
73.8%
White60
 
10.9%
Asian53
 
9.6%
Other16
 
2.9%
Coloured14
 
2.5%
(Missing)1
 
0.2%

Length

2025-11-11T13:28:28.259215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T13:28:28.954990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
black406
74.0%
white60
 
10.9%
asian53
 
9.7%
other16
 
2.9%
coloured14
 
2.6%

Most occurring characters

ValueCountFrequency (%)
a459
16.5%
l420
15.1%
B406
14.6%
c406
14.6%
k406
14.6%
i113
 
4.1%
e90
 
3.2%
t76
 
2.7%
h76
 
2.7%
W60
 
2.2%
Other values (9)275
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)2787
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a459
16.5%
l420
15.1%
B406
14.6%
c406
14.6%
k406
14.6%
i113
 
4.1%
e90
 
3.2%
t76
 
2.7%
h76
 
2.7%
W60
 
2.2%
Other values (9)275
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2787
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a459
16.5%
l420
15.1%
B406
14.6%
c406
14.6%
k406
14.6%
i113
 
4.1%
e90
 
3.2%
t76
 
2.7%
h76
 
2.7%
W60
 
2.2%
Other values (9)275
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2787
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a459
16.5%
l420
15.1%
B406
14.6%
c406
14.6%
k406
14.6%
i113
 
4.1%
e90
 
3.2%
t76
 
2.7%
h76
 
2.7%
W60
 
2.2%
Other values (9)275
9.9%

weight
Real number (ℝ)

High correlation 

Distinct80
Distinct (%)14.7%
Missing4
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean78.571429
Minimum43
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2025-11-11T13:28:29.821116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile52
Q165
median75
Q390
95-th percentile110
Maximum200
Range157
Interquartile range (IQR)25

Descriptive statistics

Standard deviation19.165226
Coefficient of variation (CV)0.24392106
Kurtosis3.4771334
Mean78.571429
Median Absolute Deviation (MAD)13
Skewness1.0824248
Sum42900
Variance367.3059
MonotonicityNot monotonic
2025-11-11T13:28:30.361619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7029
 
5.3%
7525
 
4.5%
8021
 
3.8%
9020
 
3.6%
6020
 
3.6%
6518
 
3.3%
8516
 
2.9%
5515
 
2.7%
9815
 
2.7%
7415
 
2.7%
Other values (70)352
64.0%
ValueCountFrequency (%)
432
 
0.4%
454
 
0.7%
463
 
0.5%
485
 
0.9%
493
 
0.5%
508
1.5%
524
 
0.7%
536
 
1.1%
542
 
0.4%
5515
2.7%
ValueCountFrequency (%)
2001
 
0.2%
1711
 
0.2%
1501
 
0.2%
1401
 
0.2%
1381
 
0.2%
1311
 
0.2%
1301
 
0.2%
1241
 
0.2%
1211
 
0.2%
1205
0.9%

Height
Real number (ℝ)

High correlation  Missing 

Distinct57
Distinct (%)10.5%
Missing9
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean1.6269871
Minimum0.01
Maximum1.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2025-11-11T13:28:30.985271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile1.5
Q11.57
median1.63
Q31.68
95-th percentile1.8
Maximum1.94
Range1.93
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.131287
Coefficient of variation (CV)0.080693326
Kurtosis49.082312
Mean1.6269871
Median Absolute Deviation (MAD)0.06
Skewness-4.6606498
Sum880.2
Variance0.017236276
MonotonicityNot monotonic
2025-11-11T13:28:31.621131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6553
 
9.6%
1.645
 
8.2%
1.5730
 
5.5%
1.6730
 
5.5%
1.730
 
5.5%
1.6827
 
4.9%
1.6324
 
4.4%
1.5821
 
3.8%
1.6219
 
3.5%
1.5919
 
3.5%
Other values (47)243
44.2%
ValueCountFrequency (%)
0.011
0.2%
0.651
0.2%
1.011
0.2%
1.051
0.2%
1.151
0.2%
1.191
0.2%
1.21
0.2%
1.251
0.2%
1.371
0.2%
1.381
0.2%
ValueCountFrequency (%)
1.941
 
0.2%
1.912
 
0.4%
1.882
 
0.4%
1.871
 
0.2%
1.862
 
0.4%
1.851
 
0.2%
1.842
 
0.4%
1.835
0.9%
1.821
 
0.2%
1.813
0.5%

Patient ID
Real number (ℝ)

High correlation  Unique 

Distinct550
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4086.7818
Minimum1001
Maximum8153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2025-11-11T13:28:32.328209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1028.45
Q11138.25
median3029.5
Q38015.75
95-th percentile8125.55
Maximum8153
Range7152
Interquartile range (IQR)6877.5

Descriptive statistics

Standard deviation2857.256
Coefficient of variation (CV)0.69914571
Kurtosis-1.5160415
Mean4086.7818
Median Absolute Deviation (MAD)1983
Skewness0.36861181
Sum2247730
Variance8163911.6
MonotonicityStrictly increasing
2025-11-11T13:28:32.979094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81531
 
0.2%
10011
 
0.2%
10021
 
0.2%
10031
 
0.2%
10041
 
0.2%
10051
 
0.2%
81141
 
0.2%
81151
 
0.2%
81161
 
0.2%
81171
 
0.2%
Other values (540)540
98.2%
ValueCountFrequency (%)
10011
0.2%
10021
0.2%
10031
0.2%
10041
0.2%
10051
0.2%
10061
0.2%
10071
0.2%
10081
0.2%
10091
0.2%
10101
0.2%
ValueCountFrequency (%)
81531
0.2%
81521
0.2%
81511
0.2%
81501
0.2%
81491
0.2%
81481
0.2%
81471
0.2%
81461
0.2%
81451
0.2%
81441
0.2%

original_record_index
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct550
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean276.49091
Minimum0
Maximum556
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2025-11-11T13:28:33.677734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.45
Q1137.25
median274.5
Q3418.75
95-th percentile528.55
Maximum556
Range556
Interquartile range (IQR)281.5

Descriptive statistics

Standard deviation161.39389
Coefficient of variation (CV)0.58372223
Kurtosis-1.2011279
Mean276.49091
Median Absolute Deviation (MAD)141
Skewness0.022982324
Sum152070
Variance26047.988
MonotonicityStrictly increasing
2025-11-11T13:28:34.108384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5561
 
0.2%
01
 
0.2%
11
 
0.2%
21
 
0.2%
31
 
0.2%
41
 
0.2%
5171
 
0.2%
5181
 
0.2%
5191
 
0.2%
5201
 
0.2%
Other values (540)540
98.2%
ValueCountFrequency (%)
01
0.2%
11
0.2%
21
0.2%
31
0.2%
41
0.2%
51
0.2%
61
0.2%
71
0.2%
81
0.2%
91
0.2%
ValueCountFrequency (%)
5561
0.2%
5551
0.2%
5541
0.2%
5531
0.2%
5521
0.2%
5511
0.2%
5501
0.2%
5491
0.2%
5481
0.2%
5471
0.2%

date
Date

Distinct117
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
Minimum2020-04-22 00:00:00
Maximum2021-08-10 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T13:28:34.929473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:28:36.106601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct117
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
Minimum2020-04-22 00:00:00
Maximum2021-08-10 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T13:28:37.272448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:28:38.471535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

BMI (kg/m²)
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct440
Distinct (%)81.3%
Missing9
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean1601.4229
Minimum16.184275
Maximum850000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2025-11-11T13:28:39.386432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum16.184275
5-th percentile19.721037
Q124.163265
median28.228386
Q333.910035
95-th percentile42.416898
Maximum850000
Range849983.82
Interquartile range (IQR)9.7467693

Descriptive statistics

Standard deviation36543.055
Coefficient of variation (CV)22.819116
Kurtosis540.99973
Mean1601.4229
Median Absolute Deviation (MAD)4.7908865
Skewness23.259398
Sum866369.81
Variance1.3353949 × 109
MonotonicityNot monotonic
2025-11-11T13:28:40.002863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.711662087
 
1.3%
27.548209377
 
1.3%
36.512637434
 
0.7%
32.888888894
 
0.7%
34.3754
 
0.7%
27.343754
 
0.7%
31.221303954
 
0.7%
32.87310983
 
0.5%
31.253
 
0.5%
23.124670373
 
0.5%
Other values (430)498
90.5%
(Missing)9
 
1.6%
ValueCountFrequency (%)
16.184274911
0.2%
16.7968751
0.2%
16.896235081
0.2%
16.975308641
0.2%
17.71541951
0.2%
17.959183671
0.2%
18.066167341
0.2%
18.365472912
0.4%
18.491124261
0.2%
18.818924311
0.2%
ValueCountFrequency (%)
8500001
0.2%
404.73372781
0.2%
961
0.2%
83.32516421
0.2%
70.025559331
0.2%
67.296786391
0.2%
60.386473431
0.2%
54.421768711
0.2%
52.962361421
0.2%
51.530612241
0.2%

Height (m)
Real number (ℝ)

High correlation  Missing 

Distinct57
Distinct (%)10.5%
Missing9
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean1.6269871
Minimum0.01
Maximum1.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2025-11-11T13:28:40.646802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile1.5
Q11.57
median1.63
Q31.68
95-th percentile1.8
Maximum1.94
Range1.93
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.131287
Coefficient of variation (CV)0.080693326
Kurtosis49.082312
Mean1.6269871
Median Absolute Deviation (MAD)0.06
Skewness-4.6606498
Sum880.2
Variance0.017236276
MonotonicityNot monotonic
2025-11-11T13:28:41.533755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6553
 
9.6%
1.645
 
8.2%
1.5730
 
5.5%
1.6730
 
5.5%
1.730
 
5.5%
1.6827
 
4.9%
1.6324
 
4.4%
1.5821
 
3.8%
1.6219
 
3.5%
1.5919
 
3.5%
Other values (47)243
44.2%
ValueCountFrequency (%)
0.011
0.2%
0.651
0.2%
1.011
0.2%
1.051
0.2%
1.151
0.2%
1.191
0.2%
1.21
0.2%
1.251
0.2%
1.371
0.2%
1.381
0.2%
ValueCountFrequency (%)
1.941
 
0.2%
1.912
 
0.4%
1.882
 
0.4%
1.871
 
0.2%
1.862
 
0.4%
1.851
 
0.2%
1.842
 
0.4%
1.835
0.9%
1.821
 
0.2%
1.813
0.5%

Weight (kg)
Real number (ℝ)

High correlation 

Distinct80
Distinct (%)14.7%
Missing4
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean78.571429
Minimum43
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2025-11-11T13:28:42.218997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile52
Q165
median75
Q390
95-th percentile110
Maximum200
Range157
Interquartile range (IQR)25

Descriptive statistics

Standard deviation19.165226
Coefficient of variation (CV)0.24392106
Kurtosis3.4771334
Mean78.571429
Median Absolute Deviation (MAD)13
Skewness1.0824248
Sum42900
Variance367.3059
MonotonicityNot monotonic
2025-11-11T13:28:42.733802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7029
 
5.3%
7525
 
4.5%
8021
 
3.8%
9020
 
3.6%
6020
 
3.6%
6518
 
3.3%
8516
 
2.9%
5515
 
2.7%
9815
 
2.7%
7415
 
2.7%
Other values (70)352
64.0%
ValueCountFrequency (%)
432
 
0.4%
454
 
0.7%
463
 
0.5%
485
 
0.9%
493
 
0.5%
508
1.5%
524
 
0.7%
536
 
1.1%
542
 
0.4%
5515
2.7%
ValueCountFrequency (%)
2001
 
0.2%
1711
 
0.2%
1501
 
0.2%
1401
 
0.2%
1381
 
0.2%
1311
 
0.2%
1301
 
0.2%
1241
 
0.2%
1211
 
0.2%
1205
0.9%

Last height recorded (m)
Real number (ℝ)

High correlation  Missing 

Distinct39
Distinct (%)13.9%
Missing270
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean1.6322143
Minimum1.31
Maximum1.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2025-11-11T13:28:43.301792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.31
5-th percentile1.51
Q11.58
median1.63
Q31.68
95-th percentile1.78
Maximum1.91
Range0.6
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.080193027
Coefficient of variation (CV)0.049131433
Kurtosis0.91187203
Mean1.6322143
Median Absolute Deviation (MAD)0.05
Skewness0.19489568
Sum457.02
Variance0.0064309217
MonotonicityNot monotonic
2025-11-11T13:28:43.835063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1.6525
 
4.5%
1.5920
 
3.6%
1.617
 
3.1%
1.6215
 
2.7%
1.6814
 
2.5%
1.5612
 
2.2%
1.5812
 
2.2%
1.6112
 
2.2%
1.6612
 
2.2%
1.6411
 
2.0%
Other values (29)130
23.6%
(Missing)270
49.1%
ValueCountFrequency (%)
1.311
 
0.2%
1.431
 
0.2%
1.452
 
0.4%
1.481
 
0.2%
1.55
0.9%
1.515
0.9%
1.524
0.7%
1.538
1.5%
1.546
1.1%
1.558
1.5%
ValueCountFrequency (%)
1.911
 
0.2%
1.861
 
0.2%
1.831
 
0.2%
1.812
 
0.4%
1.85
0.9%
1.791
 
0.2%
1.785
0.9%
1.771
 
0.2%
1.764
0.7%
1.758
1.5%

Last weight recorded (kg)
Real number (ℝ)

High correlation  Missing 

Distinct79
Distinct (%)27.7%
Missing265
Missing (%)48.2%
Infinite0
Infinite (%)0.0%
Mean79.610526
Minimum43
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2025-11-11T13:28:44.479434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile50.2
Q168
median77
Q394
95-th percentile110
Maximum150
Range107
Interquartile range (IQR)26

Descriptive statistics

Standard deviation18.31183
Coefficient of variation (CV)0.2300177
Kurtosis0.14267751
Mean79.610526
Median Absolute Deviation (MAD)13
Skewness0.38825183
Sum22689
Variance335.32313
MonotonicityNot monotonic
2025-11-11T13:28:45.040303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7611
 
2.0%
9510
 
1.8%
8510
 
1.8%
969
 
1.6%
709
 
1.6%
748
 
1.5%
738
 
1.5%
758
 
1.5%
947
 
1.3%
777
 
1.3%
Other values (69)198
36.0%
(Missing)265
48.2%
ValueCountFrequency (%)
431
 
0.2%
441
 
0.2%
451
 
0.2%
462
0.4%
472
0.4%
482
0.4%
492
0.4%
504
0.7%
512
0.4%
522
0.4%
ValueCountFrequency (%)
1501
0.2%
1311
0.2%
1251
0.2%
1241
0.2%
1212
0.4%
1201
0.2%
1171
0.2%
1161
0.2%
1151
0.2%
1141
0.2%

Interactions

2025-11-11T13:27:55.260579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:20:58.361441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:24.629209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:51.891789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:18.739250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:45.509154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:02.490892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:26.276635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:48.970286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:13.789685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:39.020423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:55.895651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:20:58.659795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:25.184279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:52.507815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:19.410658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:23:05.335465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:03.039590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:26.808246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:49.584862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:14.345252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:39.794041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:56.501038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:20:59.201975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:25.731807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:53.184470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:20.183779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:23:25.669849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:03.674328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:27.414159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:50.286236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:14.962931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:40.482507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:57.053444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:20:59.782271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:26.426867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:53.812094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:21.016662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:23:44.903806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:04.333045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:28.063229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:51.036967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:15.562603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:41.112520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:57.646181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:00.490033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:27.240522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:54.679511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:21.760734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:24:03.836382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:05.099658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:28.797741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:51.741654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:16.312119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:41.712109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:28:07.519001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:20.480258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:47.415191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:14.518185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:40.839972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:24:34.573660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:22.030320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:45.127191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:09.586792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:34.452626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:50.819716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:28:08.336206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:21.125429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:48.156517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:15.251803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:41.675838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:24:52.360914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:22.603013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:45.803972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:10.370894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:35.245109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:51.626253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:28:08.989693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:21.748885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:48.842762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:15.996382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:42.507657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:25:09.856273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:23.275432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:46.353915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:11.117537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:36.036808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:52.293141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:28:09.649761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:22.481787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:49.673323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:16.750306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:43.335627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:25:26.280434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:24.040104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:47.098083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:11.809319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:36.933967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:53.237991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:28:10.265922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:23.114705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:50.411985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:17.428540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:44.145133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:25:43.526204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:24.730239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:47.773925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:12.584155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:37.607181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:53.874492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:28:11.065515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:23.899144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:21:51.184254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:18.141181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:22:44.822304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:25:53.607336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:25.522824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:26:48.402741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:13.188941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:38.360749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T13:27:54.514565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-11T13:28:45.495547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Age (at enrolment)BMI (kg/m²)HeightHeight (m)Last height recorded (m)Last weight recorded (kg)Patient IDRaceSexWeight (kg)monthoriginal_record_indexseasonweightyear
Age (at enrolment)1.0000.380-0.159-0.159-0.1610.318-0.0210.1820.1410.298-0.012-0.0210.0000.2980.040
BMI (kg/m²)0.3801.000-0.210-0.210-0.0440.8710.0290.0000.0000.8760.0180.0290.0000.8760.000
Height-0.159-0.2101.0001.0000.6690.301-0.1040.0910.3120.237-0.044-0.1040.0000.2370.060
Height (m)-0.159-0.2101.0001.0000.6690.301-0.1040.0910.3120.237-0.044-0.1040.0000.2370.060
Last height recorded (m)-0.161-0.0440.6690.6691.0000.205-0.0360.2230.5560.222-0.006-0.0360.0000.2221.000
Last weight recorded (kg)0.3180.8710.3010.3010.2051.000-0.1580.1560.0030.967-0.075-0.1580.1520.9671.000
Patient ID-0.0210.029-0.104-0.104-0.036-0.1581.0001.0001.000-0.0010.4941.0001.000-0.0011.000
Race0.1820.0000.0910.0910.2230.1561.0001.0000.2120.1580.0700.1370.0500.1580.102
Sex0.1410.0000.3120.3120.5560.0031.0000.2121.0000.0370.0860.0680.0000.0370.033
Weight (kg)0.2980.8760.2370.2370.2220.967-0.0010.1580.0371.000-0.004-0.0010.0341.0000.132
month-0.0120.018-0.044-0.044-0.006-0.0750.4940.0700.086-0.0041.0000.4940.996-0.0040.644
original_record_index-0.0210.029-0.104-0.104-0.036-0.1581.0000.1370.068-0.0010.4941.0000.612-0.0010.962
season0.0000.0000.0000.0000.0000.1521.0000.0500.0000.0340.9960.6121.0000.0340.442
weight0.2980.8760.2370.2370.2220.967-0.0010.1580.0371.000-0.004-0.0010.0341.0000.132
year0.0400.0000.0600.0601.0001.0001.0000.1020.0330.1320.6440.9620.4420.1321.000

Missing values

2025-11-11T13:28:11.820101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-11T13:28:14.043685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-11T13:28:16.696817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

anonymous_patient_idprimary_dateyearmonthseasonAge (at enrolment)SexRaceweightHeightPatient IDoriginal_record_indexdateprimary_date_parsedBMI (kg/m²)Height (m)Weight (kg)Last height recorded (m)Last weight recorded (kg)
7486HEAT_49477AEB64E02020-04-2820204Autumn29.288889MaleWhite73.01.7310010.02020-04-282020-04-2824.3910591.7373.0NaNNaN
7487HEAT_31A1934F78E62020-04-2820204Autumn31.547222FemaleOther66.01.7610021.02020-04-282020-04-2821.3068181.7666.0NaNNaN
7488HEAT_BAEF29F1C6B22020-04-2820204Autumn33.241667MaleWhite79.01.6910032.02020-04-282020-04-2827.6600961.6979.01.7781.0
7489HEAT_2E24227DBBA02020-04-2820204Autumn33.319444FemaleBlack70.01.7210043.02020-04-282020-04-2823.6614391.7270.01.7268.0
7490HEAT_507153987D522020-04-2820204Autumn36.905556FemaleWhite60.01.6810054.02020-04-282020-04-2821.2585031.6860.01.6770.0
7491HEAT_5FF18C87D1CD2020-04-2820204Autumn37.619444FemaleWhite88.01.6810065.02020-04-282020-04-2831.1791381.6888.01.5886.0
7492HEAT_85257AD4944D2020-04-2820204Autumn34.775000FemaleOther58.01.6810076.02020-04-282020-04-2820.5498871.6858.0NaNNaN
7493HEAT_C7EB47BA9D282020-04-2820204Autumn28.816667MaleWhite91.01.9110087.02020-04-282020-04-2824.9444921.9191.0NaNNaN
7494HEAT_CAC5E50012B32020-04-2820204Autumn27.613889MaleColoured75.01.8010098.02020-04-282020-04-2823.1481481.8075.0NaNNaN
7495HEAT_07F3732D0E8C2020-04-2820204Autumn33.052778MaleWhite100.01.7510109.02020-04-282020-04-2832.6530611.75100.0NaNNaN
anonymous_patient_idprimary_dateyearmonthseasonAge (at enrolment)SexRaceweightHeightPatient IDoriginal_record_indexdateprimary_date_parsedBMI (kg/m²)Height (m)Weight (kg)Last height recorded (m)Last weight recorded (kg)
8026HEAT_85ADA9D5FC612021-08-0320218Winter43.366667FemaleBlack80.01.538144547.02021-08-032021-08-0334.1748901.5380.0NaNNaN
8027HEAT_5F393BBD189D2021-08-0420218Winter61.286111FemaleBlack115.01.388145548.02021-08-042021-08-0460.3864731.38115.0NaNNaN
8028HEAT_06F5DE007F782021-08-0520218Winter45.611111FemaleBlack101.01.708146549.02021-08-052021-08-0534.9480971.70101.0NaNNaN
8029HEAT_8472925512AE2021-08-0520218Winter42.822222FemaleBlack65.01.598147550.02021-08-052021-08-0525.7110081.5965.0NaNNaN
8030HEAT_DB533D0EE3142021-08-0520218Winter51.077778FemaleBlack75.01.638148551.02021-08-052021-08-0528.2283861.6375.0NaNNaN
8031HEAT_F328F887D12B2021-08-0520218Winter29.894444FemaleBlack101.01.408149552.02021-08-052021-08-0551.5306121.40101.0NaNNaN
8032HEAT_B243F719D9CA2021-08-0620218Winter37.444444FemaleBlack75.01.638150553.02021-08-062021-08-0628.2283861.6375.0NaNNaN
8033HEAT_692C50CD772B2021-08-0620218Winter46.769444FemaleBlack82.01.538151554.02021-08-062021-08-0635.0292621.5382.0NaNNaN
8034HEAT_758B225973E92021-08-1020218Winter38.644444FemaleBlack73.01.508152555.02021-08-102021-08-1032.4444441.5073.0NaNNaN
8035HEAT_3A0A597F2AB22021-08-1020218Winter44.480556MaleBlack108.01.698153556.02021-08-102021-08-1037.8138021.69108.0NaNNaN